Major breakthroughs in neuroscience have been achieved through the application of computational models to empirical research. Models are essential to connect theory to behavior and the increasingly rich and complex measures of nervous function at multiple spatial and temporal scales. That said, modeling is a highly complex activity requiring extensive training and multiple skills sets, which has created a critica shortfall in the cadre of researchers with the requisite skills to meet the modeling needs in computational neuroscience. The goal of the Summer School in Computational Sensory-Motor Neuroscience (CoSMo) is to provide cross-disciplinary training in mathematical modeling techniques relevant to understanding brain function, dysfunction and treatment. In a unique approach bridging experimental research, clinical pathology, cutting-edge technology and computer simulations, students will learn how to translate ideas and empirical findings into mathematical models. Students will gain a profound understanding of the brain's working principles and diseases using advanced modeling techniques in hands-on simulations of models during tutored sessions. This deep brain camp aims at propelling promising students into world-class researchers. Sensory and movement research form both a key paradigm in brain research and drive progress in many clinical areas related to disease and dysfunction. It is a mature area with a long history of achievements in developing, testing, and integrating experimental, neurobiological, neurotechnological and a rich array of computational modeling successes to understanding the brain. While many summer schools exist in related disciplines, CoSMo is the only summer school focusing on this exciting multidisciplinary area. It also has a unique pedagogical format that coherently spans hands-on model development, modeling methods, and integrating modeling with experiments, data analysis and clinical applications. CoSMo thus fills an important gap and teaches computational, experimental and clinical knowledge through combined empirical-theoretical teaching modules. Relevance: Participants also learn how to apply concepts to clinical pathologies using computational modeling, which results in practical and transferable skills. By developing this missing piece in the current training environment, we are accelerating progress of crucial basic and medical importance. We will train students and postdocs to use the power of computational and experimental frameworks to understand brain dysfunction. This exceptional theory-based translational component will put our students at the forefront of innovation in basic and applied brain science.